In [16]:
"""
Uses tf.contrib.data module which is in release candidate 1.2.0rc0
Based on:
- PyTorch example from Justin Johnson:
https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c
- https://gist.github.com/omoindrot/dedc857cdc0e680dfb1be99762990c9c
Required packages: tensorflow (v1.2)
You can install the release candidate 1.2.0rc0 here:
https://www.tensorflow.org/versions/r1.2/install/
Download the weights trained on ImageNet for VGG:
```
wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz
tar -xvf vgg_16_2016_08_28.tar.gz
rm vgg_16_2016_08_28.tar.gz
```
"""
%matplotlib inline
from matplotlib import pyplot as plt
import argparse
import os
import datetime
import tensorflow as tf
import tensorflow.contrib.slim as slim
import tensorflow.contrib.slim.nets
from vggnet_utils import *
VGG_MEAN = [123.68, 116.78, 103.94]
print(tf.__version__)
1.2.0-rc1
In [97]:
def leaky_relu(x, alpha=0.01):
"""Compute the leaky ReLU activation function.
Inputs:
- x: TensorFlow Tensor with arbitrary shape
- alpha: leak parameter for leaky ReLU
Returns:
TensorFlow Tensor with the same shape as x
"""
# TODO: implement leaky ReLU
return(tf.maximum((alpha*x), x))
In [105]:
experiments = [] # list with dict of params for each experiment
lr1s = [1e-3] # 5e-5,
lr2s = [1e-5] # 1e-6, 1e-7,
dropout_probs = [0.4] # 0.5, 0.3
weight_decays = [1e-4] # 5e-4,
# create a dict of experiments
for lr1 in lr1s:
for lr2 in lr2s:
for dp in dropout_probs:
for wd in weight_decays:
experiments.append({'lr1': lr1, 'lr2': lr2, 'dp': dp, 'wd': wd})
print(experiments)
# Get the list of filenames and corresponding list of labels for training et validation
# train_filenames, train_labels = list_images(args.train_dir)
# val_filenames, val_labels = list_images(args.val_dir)
all_filenames, all_labels = list_images(args.train_dir)
train_filenames, train_labels, val_filenames, val_labels = split_samples(all_filenames, all_labels)
num_classes = 17
num_val = len(val_labels)
# Training log
loss_log = []
f_log = []
thresh_log = []
predicted_logits = []
actual_labels = []
for params_dict in experiments:
print(params_dict)
parser = argparse.ArgumentParser()
parser.add_argument('--train_dir', default='data/train-jpg/')
parser.add_argument('--model_path', default='vgg_16.ckpt', type=str)
parser.add_argument('--batch_size', default=100, type=int) #32
parser.add_argument('--num_workers', default=50, type=int) #4
parser.add_argument('--num_epochs1', default=15, type=int) #10
parser.add_argument('--num_epochs2', default=10, type=int) #10
parser.add_argument('--learning_rate1', default = params_dict['lr1'], type=float) #1e-3
parser.add_argument('--learning_rate2', default = params_dict['lr2'], type=float)
parser.add_argument('--dropout_keep_prob', default = params_dict['dp'], type=float)
parser.add_argument('--weight_decay', default = params_dict['wd'], type=float)
# For accessing args in an ipython notebook
import sys; sys.argv=['']; del sys
args = parser.parse_args()
# --------------------------------------------------------------------------
# In TensorFlow, you first want to define the computation graph with all the
# necessary operations: loss, training op, accuracy...
# Any tensor created in the `graph.as_default()` scope will be part of `graph`
graph = tf.Graph()
with graph.as_default():
# Standard preprocessing for VGG on ImageNet taken from here:
# https://github.com/tensorflow/models/blob/master/slim/preprocessing/vgg_preprocessing.py
# Also see the VGG paper for more details: https://arxiv.org/pdf/1409.1556.pdf
# Preprocessing (for both training and validation):
# (1) Decode the image from jpg format
# (2) Resize the image so its smaller side is 256 pixels long
def _parse_function(filename, label):
image_string = tf.read_file(filename)
image_decoded = tf.image.decode_jpeg(image_string, channels=3) # (1)
image = tf.cast(image_decoded, tf.float32)
smallest_side = 256.0
height, width = tf.shape(image)[0], tf.shape(image)[1]
height = tf.to_float(height)
width = tf.to_float(width)
scale = tf.cond(tf.greater(height, width),
lambda: smallest_side / width,
lambda: smallest_side / height)
new_height = tf.to_int32(height * scale)
new_width = tf.to_int32(width * scale)
resized_image = tf.image.resize_images(image, [new_height, new_width]) # (2)
return resized_image, label
# Preprocessing (for training)
# (3) Take a random 224x224 crop to the scaled image
# (4) Horizontally flip the image with probability 1/2
# (5) Substract the per color mean `VGG_MEAN`
# Note: we don't normalize the data here, as VGG was trained without normalization
def training_preprocess(image, label):
crop_image = tf.random_crop(image, [224, 224, 3]) # (3)
flip_image = tf.image.random_flip_left_right(crop_image) # (4)
means = tf.reshape(tf.constant(VGG_MEAN), [1, 1, 3])
centered_image = flip_image - means # (5)
return centered_image, label
# Preprocessing (for validation)
# (3) Take a central 224x224 crop to the scaled image
# (4) Substract the per color mean `VGG_MEAN`
# Note: we don't normalize the data here, as VGG was trained without normalization
def val_preprocess(image, label):
crop_image = tf.image.resize_image_with_crop_or_pad(image, 224, 224) # (3)
means = tf.reshape(tf.constant(VGG_MEAN), [1, 1, 3])
centered_image = crop_image - means # (4)
return centered_image, label
# ----------------------------------------------------------------------
# DATASET CREATION using tf.contrib.data.Dataset
# https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/data
# The tf.contrib.data.Dataset framework uses queues in the background to feed in
# data to the model.
# We initialize the dataset with a list of filenames and labels, and then apply
# the preprocessing functions described above.
# Behind the scenes, queues will load the filenames, preprocess them with multiple
# threads and apply the preprocessing in parallel, and then batch the data
# Training dataset
train_filenames = tf.constant(train_filenames)
train_labels = tf.constant(train_labels)
train_dataset = tf.contrib.data.Dataset.from_tensor_slices((train_filenames, train_labels))
train_dataset = train_dataset.map(_parse_function,
num_threads=args.num_workers, output_buffer_size=args.batch_size)
train_dataset = train_dataset.map(training_preprocess,
num_threads=args.num_workers, output_buffer_size=args.batch_size)
train_dataset = train_dataset.shuffle(buffer_size=10000) # don't forget to shuffle
batched_train_dataset = train_dataset.batch(args.batch_size)
# Validation dataset
val_filenames = tf.constant(val_filenames)
val_labels = tf.constant(val_labels)
val_dataset = tf.contrib.data.Dataset.from_tensor_slices((val_filenames, val_labels))
val_dataset = val_dataset.map(_parse_function,
num_threads=args.num_workers, output_buffer_size=args.batch_size)
val_dataset = val_dataset.map(val_preprocess,
num_threads=args.num_workers, output_buffer_size=args.batch_size)
batched_val_dataset = val_dataset.batch(args.batch_size)
# Now we define an iterator that can operator on either dataset.
# The iterator can be reinitialized by calling:
# - sess.run(train_init_op) for 1 epoch on the training set
# - sess.run(val_init_op) for 1 epoch on the valiation set
# Once this is done, we don't need to feed any value for images and labels
# as they are automatically pulled out from the iterator queues.
# A reinitializable iterator is defined by its structure. We could use the
# `output_types` and `output_shapes` properties of either `train_dataset`
# or `validation_dataset` here, because they are compatible.
iterator = tf.contrib.data.Iterator.from_structure(batched_train_dataset.output_types,
batched_train_dataset.output_shapes)
images, labels = iterator.get_next()
train_init_op = iterator.make_initializer(batched_train_dataset)
val_init_op = iterator.make_initializer(batched_val_dataset)
# Indicates whether we are in training or in test mode
is_training = tf.placeholder(tf.bool)
# ---------------------------------------------------------------------
# Now that we have set up the data, it's time to set up the model.
# For this example, we'll use VGG-16 pretrained on ImageNet. We will remove the
# last fully connected layer (fc8) and replace it with our own, with an
# output size num_classes=8
# We will first train the last layer for a few epochs.
# Then we will train the entire model on our dataset for a few epochs.
# Get the pretrained model, specifying the num_classes argument to create a new
# fully connected replacing the last one, called "vgg_16/fc8"
# Each model has a different architecture, so "vgg_16/fc8" will change in another model.
# Here, logits gives us directly the predicted scores we wanted from the images.
# We pass a scope to initialize "vgg_16/fc8" weights with he_initializer
vgg = tf.contrib.slim.nets.vgg
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=args.weight_decay)):
_, layers = vgg.vgg_16(images, num_classes=num_classes, is_training=is_training,
dropout_keep_prob=args.dropout_keep_prob)
# _ output logits
# add some extra layers after fc6
# Out of the arg scope
vgg_fc6_tensor = layers['vgg_16/fc6']
# print(vgg_fc6_tensor.shape)
with tf.variable_scope("extra_layers"):
extra_fc1 = tf.layers.dense(vgg_fc6_tensor, 4096, name="extra_fc1", activation = tf.nn.relu, trainable = True) #1024
extra_bn1 = tf.layers.batch_normalization(extra_fc1, axis = -1, training=True, name = "extra_bn1")
extra_conv1 = tf.layers.conv2d(tf.reshape(extra_bn1, shape = [-1, 64, 64, 1]), filters = 32, kernel_size = [5, 5],
strides = 1, activation = leaky_relu, name = "extra_conv1")
extra_pool1 = tf.layers.max_pooling2d(extra_conv1, pool_size = [2, 2], strides = 2, name = "extra_pool1")
extra_conv2 = tf.layers.conv2d(extra_pool1, filters = 64, kernel_size = [5, 5],
strides = 1, activation = leaky_relu, name = "extra_conv2")
extra_pool2 = tf.layers.max_pooling2d(extra_conv2, pool_size = [2, 2], strides = 2, name = "extra_pool2")
print(extra_pool2.shape)
extra_pool2_flat = tf.reshape(extra_pool2, [-1, 13*13*64], name = "extra_pool2_flat")
extra_conn1 = tf.layers.dense(extra_pool2_flat, units = 13*13*64, activation = leaky_relu, name = "extra_conn1")
logits = tf.layers.dense(extra_conn1, num_classes, name="logits", activation = tf.sigmoid, trainable = True)
# extra_fc2 = tf.layers.dense(extra_fc1, 1024, name="extra_fc2", trainable = True)
# extra_fc3 = tf.layers.dense(extra_fc2, 1024, name="extra_fc3", trainable = True)
# extra_fc4 = tf.layers.dense(extra_fc3, 256, name="extra_fc4", trainable = True)
# logits = tf.layers.dense(extra_fc4, num_classes, name="logits", activation = tf.sigmoid, trainable = True)
# Specify where the model checkpoint is (pretrained weights).
model_path = args.model_path
assert(os.path.isfile(model_path))
# Restore only the layers up to fc6 (included)
# Calling function `init_fn(sess)` will load all the pretrained weights.
variables_to_restore = tf.contrib.framework.get_variables_to_restore(exclude=['vgg_16/fc8', 'vgg_16/fc7',
"extra_layers/extra_fc1",
"extra_layers/extra_fc1",
"extra_layers/extra_bn1",
"extra_layers/extra_conv1",
"extra_layers/extra_pool1",
"extra_layers/extra_conv2",
"extra_layers/extra_pool2",
"extra_layers/extra_pool2_flat",
"extra_layers/extra_conn1",
# "extra_layers/extra_fc2",
# "extra_layers/extra_fc3",
# "extra_layers/extra_fc4",
# "extra_layers/extra_fc5",
# "extra_layers/extra_fc6",
"extra_layers/logits"]) #, 'vgg_16/fc6'])
init_fn = tf.contrib.framework.assign_from_checkpoint_fn(model_path, variables_to_restore)
# Initialization operation from scratch for the new "fc6" layers
# `get_variables` will only return the variables whose name starts with the given pattern
# fc6_variables = tf.contrib.framework.get_variables('vgg_16/fc6')
# fc6_init = tf.variables_initializer(fc6_variables)
# Initialization operation from scratch for the new "fc7" layers
# `get_variables` will only return the variables whose name starts with the given pattern
fc7_variables = tf.contrib.framework.get_variables('vgg_16/fc7')
fc7_init = tf.variables_initializer(fc7_variables)
# Initialization operation from scratch for the new "fc8" layers
# `get_variables` will only return the variables whose name starts with the given pattern
fc8_variables = tf.contrib.framework.get_variables('vgg_16/fc8')
fc8_init = tf.variables_initializer(fc8_variables)
# Initialize extra layer variables
extras_init = tf.variables_initializer(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'extra*'))
# ---------------------------------------------------------------------
# Using tf.losses, any loss is added to the tf.GraphKeys.LOSSES collection
# We can then call the total loss easily
# tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=tf.reshape(logits, shape = [-1, num_classes])) # softmax cross entropy loss so can have labels with multiple classes
loss = tf.losses.get_total_loss()
tf.summary.scalar("loss", loss) # log the loss in summary
# First we want to train only the reinitialized last layer fc8 for a few epochs.
# We run minimize the loss only with respect to the fc8 variables (weight and bias).
# fc876_optimizer = tf.train.GradientDescentOptimizer(args.learning_rate1)
# fc876_train_op = fc876_optimizer.minimize(loss, var_list=[fc8_variables, fc7_variables, fc6_variables])
# First we want to train only the last extra layers for a few epochs.
# We minimize the loss only with respect to the extra variables (weight and bias).
extra_optimizer = tf.train.GradientDescentOptimizer(args.learning_rate1)
extra_train_op = extra_optimizer.minimize(loss, var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'extra*')) # [logits, extra_fc1, extra_fc2]) ### fc8_variables, fc7_variables])
# print(tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'extra*'))
# Then we want to finetune the entire model for a few epochs.
# We run minimize the loss only with respect to all the variables.
full_optimizer = tf.train.GradientDescentOptimizer(args.learning_rate2)
full_train_op = full_optimizer.minimize(loss)
# Evaluation metrics
# Merge all logged summaries together
merged_summary_op = tf.summary.merge_all()
tf.get_default_graph().finalize()
# --------------------------------------------------------------------------
# Now that we have built the graph and finalized it, we define the session.
# The session is the interface to *run* the computational graph.
# We can call our training operations with `sess.run(train_op)` for instance
with tf.Session(graph=graph) as sess:
now = datetime.datetime.now()
# start summary of session
train_writer = tf.summary.FileWriter('./summaries/' +
'{}-{}-{}-{}-{}'.format(now.year, now.month, now.day, now.hour, now.minute) , sess.graph)
# sess.run(tf.global_variables_initializer())
init_fn(sess) # load the pretrained weights
# For debugging
# for v in tf.global_variables():
# print(v.name)
sess.run(extras_init) # initialize extra variables
sess.run(fc8_init) # initialize the unused fc8 layer
sess.run(fc7_init) # initialize unused fc7
# sess.run(fc6_init) # initialize fc6
iteration = 0
# Update only the last layer for a few epochs.
for epoch in range(args.num_epochs1):
# Run an epoch over the training data.
print('Starting epoch %d / %d' % (epoch + 1, args.num_epochs1))
# Here we initialize the iterator with the training set.
# This means that we can go through an entire epoch until the iterator becomes empty.
sess.run(train_init_op)
while True: # and iteration < 10:
try:
_, curr_loss, curr_logits, curr_labels, summary = sess.run([extra_train_op, loss, logits, labels, merged_summary_op], {is_training: True}) # fc876_train_op,
loss_log.append(curr_loss)
train_writer.add_summary(summary, iteration)
curr_logits = np.reshape(curr_logits, [-1, num_classes])
if curr_loss < 100:
print('Current loss: %f' % curr_loss)
# Check F score on logits
threshs = [0.05, 0.1, 0.25, 0.5, 0.75, 0.8, 0.9, 0.95]
Fs = []
for i in range(len(threshs)):
Fs.append(fbeta_score(curr_labels, np.array(curr_logits) > threshs[i], beta=2, average='samples'))
max_i = Fs.index(max(Fs))
print('thresh: %f, max F: %f' %(threshs[max_i], Fs[max_i]))
thresh_log.append(threshs[max_i])
f_log.append(Fs[max_i])
iteration += 1
else:
print("EXPLODING LOSS")
break
except tf.errors.OutOfRangeError:
break
# Print current loss
print('Current epoch loss: %f' % curr_loss)
# Check F score on logits
threshs = [0.05, 0.1, 0.25, 0.5, 0.75, 0.8, 0.9, 0.95]
Fs = []
for i in range(len(threshs)):
Fs.append(fbeta_score(curr_labels, np.array(curr_logits) > threshs[i], beta=2, average='samples'))
max_i = Fs.index(max(Fs))
print('Epoch thresh: %f, max F: %f' %(threshs[max_i], Fs[max_i]))
# Check accuracy on the train and val sets every epoch.
# train_acc = check_accuracy(sess, correct_prediction, is_training, train_init_op)
# val_acc = check_accuracy(sess, correct_prediction, is_training, val_init_op)
# print('Train accuracy: %f' % train_acc)
# print('Val accuracy: %f\n' % val_acc)
# Train the entire model for a few more epochs, continuing with the *same* weights.
for epoch in range(args.num_epochs2):
print('Starting epoch %d / %d' % (epoch + 1, args.num_epochs2))
sess.run(train_init_op)
while True: # and iteration < 20:
try:
_, curr_loss, curr_logits, curr_labels, summary = sess.run([full_train_op, loss, logits, labels, merged_summary_op], {is_training: True})
loss_log.append(curr_loss)
train_writer.add_summary(summary, iteration)
curr_logits = np.reshape(curr_logits, [-1, num_classes])
if curr_loss < 100:
print('Current loss: %f' % curr_loss)
# Check F score on logits
threshs = [0.5, 0.1, 0.25, 0.5, 0.75, 0.8, 0.9, 0.95]
Fs = []
for i in range(len(threshs)):
Fs.append(fbeta_score(curr_labels, np.array(curr_logits) > threshs[i], beta=2, average='samples'))
max_i = Fs.index(max(Fs))
print('thresh: %f, max F: %f' %(threshs[max_i], Fs[max_i]))
thresh_log.append(threshs[max_i])
f_log.append(Fs[max_i])
if epoch == (args.num_epochs2-1):
predicted_logits.append(curr_logits)
actual_labels.append(curr_labels)
iteration += 1
else:
print("EXPLODING LOSS")
break
except tf.errors.OutOfRangeError:
break
# Print current loss
print('Entire model epoch loss: %f' % curr_loss)
# Check F score on logits
threshs = [0.05, 0.1, 0.25, 0.5, 0.75, 0.8, 0.9, 0.95]
Fs = []
for i in range(len(threshs)):
Fs.append(fbeta_score(curr_labels, np.array(curr_logits) > threshs[i], beta=2, average='samples'))
max_i = Fs.index(max(Fs))
print('Entire model epoch thresh: %f, max F: %f' %(threshs[max_i], Fs[max_i]))
# Check accuracy on the train and val sets every epoch
# train_acc = check_accuracy(sess, correct_prediction, is_training, train_init_op)
# val_acc = check_accuracy(sess, correct_prediction, is_training, val_init_op)
# print('Train accuracy: %f' % train_acc)
# print('Val accuracy: %f\n' % val_acc)
100%|██████████| 40479/40479 [00:00<00:00, 473856.14it/s]
[{'dp': 0.4, 'lr1': 0.001, 'lr2': 1e-05, 'wd': 0.0001}]
listed
{'dp': 0.4, 'lr1': 0.001, 'lr2': 1e-05, 'wd': 0.0001}
(?, 13, 13, 64)
WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.
Type is unsupported, or the types of the items don't match field type in CollectionDef.
'dict' object has no attribute 'name'
WARNING:tensorflow:Error encountered when serializing LAYER_NAME_UIDS.
Type is unsupported, or the types of the items don't match field type in CollectionDef.
'dict' object has no attribute 'name'
INFO:tensorflow:Restoring parameters from vgg_16.ckpt
INFO:tensorflow:Restoring parameters from vgg_16.ckpt
Starting epoch 1 / 15
Current loss: 8.412778
thresh: 0.050000, max F: 0.479282
Current loss: 8.044341
thresh: 0.500000, max F: 0.575802
Current loss: 8.384157
thresh: 0.500000, max F: 0.571492
Current loss: 8.043993
thresh: 0.500000, max F: 0.619258
Current loss: 8.128809
thresh: 0.500000, max F: 0.600588
Current loss: 8.496967
thresh: 0.500000, max F: 0.617190
Current loss: 9.091754
thresh: 0.500000, max F: 0.640518
Current loss: 8.269986
thresh: 0.500000, max F: 0.610043
Current loss: 8.354798
thresh: 0.500000, max F: 0.610030
Current loss: 8.779675
thresh: 0.500000, max F: 0.573935
Current loss: 7.702882
thresh: 0.500000, max F: 0.586381
Current loss: 8.467604
thresh: 0.500000, max F: 0.651979
Current loss: 8.184071
thresh: 0.500000, max F: 0.614028
Current loss: 8.580548
thresh: 0.500000, max F: 0.637821
Current loss: 8.410450
thresh: 0.500000, max F: 0.605375
Current loss: 8.410397
thresh: 0.500000, max F: 0.602128
Current loss: 8.155311
thresh: 0.500000, max F: 0.579709
Current loss: 7.928412
thresh: 0.500000, max F: 0.603507
Current loss: 7.814952
thresh: 0.500000, max F: 0.614926
Current loss: 8.664599
thresh: 0.500000, max F: 0.635640
Current loss: 8.919362
thresh: 0.500000, max F: 0.650923
Current loss: 8.041209
thresh: 0.500000, max F: 0.612723
Current loss: 8.805675
thresh: 0.500000, max F: 0.625402
Current loss: 8.437357
thresh: 0.500000, max F: 0.596667
Current loss: 8.465627
thresh: 0.500000, max F: 0.621490
Current loss: 8.295376
thresh: 0.500000, max F: 0.618333
Current loss: 8.408386
thresh: 0.500000, max F: 0.621598
Current loss: 8.408611
thresh: 0.500000, max F: 0.596133
Current loss: 8.493326
thresh: 0.500000, max F: 0.614897
Current loss: 7.841663
thresh: 0.500000, max F: 0.580575
Current loss: 8.351462
thresh: 0.500000, max F: 0.633958
Current loss: 8.294173
thresh: 0.500000, max F: 0.645580
Current loss: 8.889090
thresh: 0.500000, max F: 0.641647
Current loss: 8.407555
thresh: 0.500000, max F: 0.603299
Current loss: 8.435573
thresh: 0.500000, max F: 0.594479
Current loss: 8.973544
thresh: 0.500000, max F: 0.655784
Current loss: 8.236876
thresh: 0.500000, max F: 0.612887
Current loss: 8.491632
thresh: 0.500000, max F: 0.623051
Current loss: 8.378324
thresh: 0.500000, max F: 0.609861
Current loss: 8.548287
thresh: 0.500000, max F: 0.613581
Current loss: 8.604265
thresh: 0.500000, max F: 0.634241
Current loss: 8.576083
thresh: 0.500000, max F: 0.632723
Current loss: 8.094329
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Current epoch loss: 8.610212
Epoch thresh: 0.500000, max F: 0.629503
Starting epoch 2 / 15
Current loss: 8.034856
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Current loss: 7.986707
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Current epoch loss: 7.986707
Epoch thresh: 0.500000, max F: 0.634307
Starting epoch 3 / 15
Current loss: 7.615744
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Current loss: 7.317822
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Current loss: 7.842297
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Current epoch loss: 7.842297
Epoch thresh: 0.750000, max F: 0.680265
Starting epoch 4 / 15
Current loss: 8.189267
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Current loss: 7.888108
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Current loss: 6.994947
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Current loss: 7.394424
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Current epoch loss: 7.394424
Epoch thresh: 0.900000, max F: 0.666442
Starting epoch 5 / 15
Current loss: 7.270384
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Current loss: 7.478535
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Current loss: 7.360885
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Current epoch loss: 7.360885
Epoch thresh: 0.950000, max F: 0.697063
Starting epoch 6 / 15
Current loss: 7.536042
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Current loss: 8.462052
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Current epoch loss: 7.485409
Epoch thresh: 0.950000, max F: 0.644649
Starting epoch 7 / 15
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Current epoch loss: 7.407598
Epoch thresh: 0.950000, max F: 0.703174
Starting epoch 8 / 15
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Current epoch loss: 7.452852
Epoch thresh: 0.800000, max F: 0.656527
Starting epoch 9 / 15
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Current loss: 8.345324
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Current epoch loss: 8.345324
Epoch thresh: 0.900000, max F: 0.698713
Starting epoch 10 / 15
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Current loss: 7.174746
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Current epoch loss: 7.174746
Epoch thresh: 0.900000, max F: 0.631195
Starting epoch 11 / 15
Current loss: 7.836689
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Current loss: 7.054315
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Current loss: 7.615295
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Current loss: 6.756338
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Current loss: 7.893475
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Current epoch loss: 7.893475
Epoch thresh: 0.900000, max F: 0.702277
Starting epoch 12 / 15
Current loss: 7.453240
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Current loss: 7.392859
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Current loss: 7.635263
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Current loss: 7.778722
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Current epoch loss: 7.778722
Epoch thresh: 0.900000, max F: 0.677031
Starting epoch 13 / 15
Current loss: 8.105868
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Current loss: 7.382200
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Current loss: 7.695623
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Current loss: 6.882417
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Current loss: 7.570006
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Current loss: 7.187007
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Current loss: 7.892442
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Current epoch loss: 7.892442
Epoch thresh: 0.900000, max F: 0.659538
Starting epoch 14 / 15
Current loss: 7.623671
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Current loss: 7.395677
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Current loss: 6.977838
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Current epoch loss: 6.977838
Epoch thresh: 0.900000, max F: 0.647275
Starting epoch 15 / 15
Current loss: 7.440124
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thresh: 0.800000, max F: 0.653135
Current loss: 7.455675
thresh: 0.800000, max F: 0.644464
Current loss: 7.594687
thresh: 0.800000, max F: 0.644405
Current loss: 7.699967
thresh: 0.800000, max F: 0.685432
Current loss: 6.814857
thresh: 0.800000, max F: 0.608036
Current loss: 7.504906
thresh: 0.800000, max F: 0.649211
Current loss: 7.985400
thresh: 0.800000, max F: 0.654444
Current loss: 7.544021
thresh: 0.800000, max F: 0.660982
Current loss: 7.728462
thresh: 0.800000, max F: 0.645779
Current loss: 7.420394
thresh: 0.800000, max F: 0.668244
Current loss: 7.200925
thresh: 0.800000, max F: 0.635595
Current loss: 7.883076
thresh: 0.800000, max F: 0.665030
Current loss: 8.115431
thresh: 0.800000, max F: 0.685595
Current loss: 8.285988
thresh: 0.800000, max F: 0.683378
Current loss: 7.492532
thresh: 0.800000, max F: 0.645625
Current loss: 7.218401
thresh: 0.800000, max F: 0.616548
Current loss: 7.050650
thresh: 0.800000, max F: 0.645119
Current loss: 7.287619
thresh: 0.800000, max F: 0.622113
Current loss: 7.635756
thresh: 0.800000, max F: 0.645685
Current loss: 7.544976
thresh: 0.800000, max F: 0.625246
Current loss: 7.035745
thresh: 0.800000, max F: 0.650789
Current loss: 7.169605
thresh: 0.800000, max F: 0.644122
Current loss: 7.319724
thresh: 0.800000, max F: 0.646414
Current loss: 7.452503
thresh: 0.800000, max F: 0.666607
Current loss: 7.088139
thresh: 0.800000, max F: 0.628690
Current loss: 7.489103
thresh: 0.800000, max F: 0.646801
Current loss: 7.369147
thresh: 0.800000, max F: 0.672381
Current loss: 7.990458
thresh: 0.800000, max F: 0.673155
Current loss: 7.712228
thresh: 0.800000, max F: 0.666131
Current loss: 7.605244
thresh: 0.800000, max F: 0.642411
Current loss: 7.255303
thresh: 0.800000, max F: 0.621607
Current loss: 7.630663
thresh: 0.800000, max F: 0.638115
Current loss: 7.245779
thresh: 0.800000, max F: 0.633661
Current loss: 7.830129
thresh: 0.800000, max F: 0.675327
Current loss: 7.765357
thresh: 0.800000, max F: 0.668824
Current loss: 7.514125
thresh: 0.800000, max F: 0.653065
Current loss: 7.400351
thresh: 0.800000, max F: 0.635833
Current loss: 7.400378
thresh: 0.800000, max F: 0.638318
Current loss: 7.909547
thresh: 0.800000, max F: 0.629643
Current loss: 7.279599
thresh: 0.800000, max F: 0.625823
Current loss: 7.786234
thresh: 0.800000, max F: 0.668839
Current loss: 7.551183
thresh: 0.800000, max F: 0.646032
Current loss: 7.138743
thresh: 0.800000, max F: 0.650357
Current loss: 7.771752
thresh: 0.800000, max F: 0.637813
Current loss: 8.161709
thresh: 0.800000, max F: 0.702381
Current loss: 7.755805
thresh: 0.800000, max F: 0.660779
Current loss: 7.261893
thresh: 0.800000, max F: 0.636845
Current loss: 7.611896
thresh: 0.800000, max F: 0.646374
Current loss: 7.247427
thresh: 0.800000, max F: 0.645312
Current loss: 7.264091
thresh: 0.800000, max F: 0.633125
Current loss: 7.275810
thresh: 0.800000, max F: 0.640089
Current loss: 7.627606
thresh: 0.800000, max F: 0.639256
Current loss: 8.034120
thresh: 0.800000, max F: 0.667773
Current loss: 7.491485
thresh: 0.800000, max F: 0.659226
Current loss: 8.002239
thresh: 0.800000, max F: 0.684196
Current loss: 7.466755
thresh: 0.800000, max F: 0.644772
Current loss: 7.479751
thresh: 0.800000, max F: 0.654077
Current loss: 7.404747
thresh: 0.800000, max F: 0.647976
Current loss: 7.438265
thresh: 0.800000, max F: 0.663720
Current loss: 7.220695
thresh: 0.800000, max F: 0.652173
Current loss: 7.277825
thresh: 0.800000, max F: 0.647073
Current loss: 7.501957
thresh: 0.800000, max F: 0.658140
Current loss: 7.622386
thresh: 0.800000, max F: 0.639033
Current loss: 7.515604
thresh: 0.800000, max F: 0.646205
Current loss: 9.073465
thresh: 0.800000, max F: 0.709370
Current loss: 7.466671
thresh: 0.800000, max F: 0.652619
Current loss: 7.425127
thresh: 0.800000, max F: 0.657237
Current loss: 7.952547
thresh: 0.800000, max F: 0.659836
Current loss: 7.884863
thresh: 0.800000, max F: 0.670565
Current loss: 7.559858
thresh: 0.800000, max F: 0.657877
Current loss: 7.288923
thresh: 0.800000, max F: 0.668929
Current loss: 7.255432
thresh: 0.800000, max F: 0.623130
Current loss: 7.536468
thresh: 0.800000, max F: 0.647292
Current loss: 7.694599
thresh: 0.800000, max F: 0.662589
Current loss: 6.919647
thresh: 0.800000, max F: 0.615744
Current loss: 6.718316
thresh: 0.800000, max F: 0.610283
Current loss: 7.664195
thresh: 0.800000, max F: 0.647946
Current loss: 7.700529
thresh: 0.800000, max F: 0.645704
Current loss: 7.558393
thresh: 0.800000, max F: 0.660496
Current loss: 7.883027
thresh: 0.800000, max F: 0.664038
Current loss: 7.627260
thresh: 0.800000, max F: 0.636161
Current loss: 7.395707
thresh: 0.800000, max F: 0.675417
Current loss: 7.537514
thresh: 0.800000, max F: 0.666577
Current loss: 7.333816
thresh: 0.800000, max F: 0.620050
Current loss: 7.105368
thresh: 0.050000, max F: 0.603115
Current loss: 7.552306
thresh: 0.800000, max F: 0.652619
Current loss: 7.928314
thresh: 0.800000, max F: 0.655060
Current loss: 7.118911
thresh: 0.800000, max F: 0.637321
Current loss: 8.028885
thresh: 0.800000, max F: 0.659792
Current loss: 8.107176
thresh: 0.800000, max F: 0.676548
Current loss: 7.745616
thresh: 0.800000, max F: 0.666151
Current loss: 7.232525
thresh: 0.800000, max F: 0.667530
Current loss: 7.506643
thresh: 0.800000, max F: 0.650625
Current loss: 7.529961
thresh: 0.800000, max F: 0.642693
Current loss: 7.633994
thresh: 0.800000, max F: 0.656270
Current loss: 7.904634
thresh: 0.800000, max F: 0.655585
Current loss: 7.606778
thresh: 0.800000, max F: 0.668810
Current loss: 7.790043
thresh: 0.800000, max F: 0.681190
Current loss: 7.599406
thresh: 0.800000, max F: 0.666339
Current loss: 7.960540
thresh: 0.800000, max F: 0.674658
Current loss: 7.988730
thresh: 0.900000, max F: 0.674345
Current loss: 7.676674
thresh: 0.800000, max F: 0.663839
Current loss: 8.066569
thresh: 0.800000, max F: 0.673497
Current loss: 7.065913
thresh: 0.800000, max F: 0.647321
Current loss: 7.689896
thresh: 0.800000, max F: 0.681369
Current loss: 8.188232
thresh: 0.800000, max F: 0.691434
Current loss: 7.850489
thresh: 0.900000, max F: 0.664673
Current loss: 7.720377
thresh: 0.900000, max F: 0.673423
Current loss: 7.008015
thresh: 0.050000, max F: 0.600231
Current loss: 7.572461
thresh: 0.900000, max F: 0.679688
Current loss: 7.450570
thresh: 0.750000, max F: 0.651164
Current loss: 7.271311
thresh: 0.900000, max F: 0.621488
Current loss: 7.669671
thresh: 0.900000, max F: 0.663735
Current loss: 8.048656
thresh: 0.900000, max F: 0.693006
Current loss: 7.473819
thresh: 0.900000, max F: 0.664077
Current loss: 7.739332
thresh: 0.900000, max F: 0.679931
Current loss: 7.315263
thresh: 0.900000, max F: 0.620938
Current loss: 7.376331
thresh: 0.900000, max F: 0.678363
Current loss: 7.573812
thresh: 0.900000, max F: 0.665417
Current loss: 8.131227
thresh: 0.900000, max F: 0.673522
Current loss: 6.486114
thresh: 0.900000, max F: 0.616101
Current loss: 7.684200
thresh: 0.900000, max F: 0.680208
Current loss: 7.496296
thresh: 0.800000, max F: 0.653571
Current loss: 7.935364
thresh: 0.800000, max F: 0.667996
Current loss: 7.438967
thresh: 0.800000, max F: 0.638423
Current loss: 7.623326
thresh: 0.800000, max F: 0.666642
Current loss: 7.385926
thresh: 0.800000, max F: 0.640923
Current loss: 7.762754
thresh: 0.800000, max F: 0.676250
Current loss: 7.102864
thresh: 0.800000, max F: 0.641696
Current loss: 8.083606
thresh: 0.800000, max F: 0.663502
Current loss: 8.085930
thresh: 0.800000, max F: 0.687803
Current loss: 7.785567
thresh: 0.800000, max F: 0.677143
Current loss: 7.923109
thresh: 0.800000, max F: 0.671711
Current loss: 7.537185
thresh: 0.800000, max F: 0.638393
Current loss: 7.483581
thresh: 0.800000, max F: 0.650208
Current loss: 7.904930
thresh: 0.750000, max F: 0.679642
Current loss: 7.493127
thresh: 0.750000, max F: 0.640632
Current loss: 7.512822
thresh: 0.900000, max F: 0.654693
Current epoch loss: 7.512822
Epoch thresh: 0.900000, max F: 0.654693
Starting epoch 1 / 10
/home/cs231n/myVE35/lib/python3.5/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in samples with no predicted labels.
'precision', 'predicted', average, warn_for)
---------------------------------------------------------------------------
ResourceExhaustedError Traceback (most recent call last)
/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1138 try:
-> 1139 return fn(*args)
1140 except errors.OpError as e:
/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
1120 feed_dict, fetch_list, target_list,
-> 1121 status, run_metadata)
1122
/usr/lib/python3.5/contextlib.py in __exit__(self, type, value, traceback)
65 try:
---> 66 next(self.gen)
67 except StopIteration:
/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py in raise_exception_on_not_ok_status()
465 compat.as_text(pywrap_tensorflow.TF_Message(status)),
--> 466 pywrap_tensorflow.TF_GetCode(status))
467 finally:
ResourceExhaustedError: OOM when allocating tensor with shape[100,64,112,112]
[[Node: gradients_1/vgg_16/pool1/MaxPool_grad/MaxPoolGrad = MaxPoolGrad[T=DT_FLOAT, data_format="NHWC", ksize=[1, 2, 2, 1], padding="VALID", strides=[1, 2, 2, 1], _device="/job:localhost/replica:0/task:0/gpu:0"](vgg_16/conv1/conv1_2/Relu, vgg_16/pool1/MaxPool, gradients_1/vgg_16/conv2/conv2_1/convolution_grad/tuple/control_dependency)]]
During handling of the above exception, another exception occurred:
ResourceExhaustedError Traceback (most recent call last)
<ipython-input-105-ddaa74b1292e> in <module>()
364 while True: # and iteration < 20:
365 try:
--> 366 _, curr_loss, curr_logits, curr_labels, summary = sess.run([full_train_op, loss, logits, labels, merged_summary_op], {is_training: True})
367 loss_log.append(curr_loss)
368 train_writer.add_summary(summary, iteration)
/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
787 try:
788 result = self._run(None, fetches, feed_dict, options_ptr,
--> 789 run_metadata_ptr)
790 if run_metadata:
791 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
995 if final_fetches or final_targets:
996 results = self._do_run(handle, final_targets, final_fetches,
--> 997 feed_dict_string, options, run_metadata)
998 else:
999 results = []
/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1130 if handle is None:
1131 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1132 target_list, options, run_metadata)
1133 else:
1134 return self._do_call(_prun_fn, self._session, handle, feed_dict,
/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1150 except KeyError:
1151 pass
-> 1152 raise type(e)(node_def, op, message)
1153
1154 def _extend_graph(self):
ResourceExhaustedError: OOM when allocating tensor with shape[100,64,112,112]
[[Node: gradients_1/vgg_16/pool1/MaxPool_grad/MaxPoolGrad = MaxPoolGrad[T=DT_FLOAT, data_format="NHWC", ksize=[1, 2, 2, 1], padding="VALID", strides=[1, 2, 2, 1], _device="/job:localhost/replica:0/task:0/gpu:0"](vgg_16/conv1/conv1_2/Relu, vgg_16/pool1/MaxPool, gradients_1/vgg_16/conv2/conv2_1/convolution_grad/tuple/control_dependency)]]
Caused by op 'gradients_1/vgg_16/pool1/MaxPool_grad/MaxPoolGrad', defined at:
File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
File "/usr/lib/python3.5/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/ipykernel/__main__.py", line 3, in <module>
app.launch_new_instance()
File "/home/cs231n/myVE35/lib/python3.5/site-packages/traitlets/config/application.py", line 592, in launch_instance
app.start()
File "/home/cs231n/myVE35/lib/python3.5/site-packages/ipykernel/kernelapp.py", line 405, in start
ioloop.IOLoop.instance().start()
File "/home/cs231n/myVE35/lib/python3.5/site-packages/zmq/eventloop/ioloop.py", line 162, in start
super(ZMQIOLoop, self).start()
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tornado/ioloop.py", line 883, in start
handler_func(fd_obj, events)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/home/cs231n/myVE35/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 260, in dispatcher
return self.dispatch_shell(stream, msg)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 212, in dispatch_shell
handler(stream, idents, msg)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/ipykernel/kernelbase.py", line 370, in execute_request
user_expressions, allow_stdin)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/ipykernel/ipkernel.py", line 175, in do_execute
shell.run_cell(code, store_history=store_history, silent=silent)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2902, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 3006, in run_ast_nodes
if self.run_code(code, result):
File "/home/cs231n/myVE35/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 3066, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-105-ddaa74b1292e>", line 275, in <module>
full_train_op = full_optimizer.minimize(loss)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py", line 315, in minimize
grad_loss=grad_loss)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/training/optimizer.py", line 386, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py", line 540, in gradients
grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py", line 346, in _MaybeCompile
return grad_fn() # Exit early
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py", line 540, in <lambda>
grad_scope, op, func_call, lambda: grad_fn(op, *out_grads))
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/ops/nn_grad.py", line 525, in _MaxPoolGrad
data_format=op.get_attr("data_format"))
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 1752, in _max_pool_grad
data_format=data_format, name=name)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
self._traceback = _extract_stack()
...which was originally created as op 'vgg_16/pool1/MaxPool', defined at:
File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main
"__main__", mod_spec)
[elided 17 identical lines from previous traceback]
File "/home/cs231n/myVE35/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 3066, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-105-ddaa74b1292e>", line 181, in <module>
dropout_keep_prob=args.dropout_keep_prob)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/contrib/slim/python/slim/nets/vgg.py", line 171, in vgg_16
net = layers_lib.max_pool2d(net, [2, 2], scope='pool1')
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1623, in max_pool2d
outputs = layer.apply(inputs)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/layers/base.py", line 490, in apply
return self.__call__(inputs, *args, **kwargs)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/layers/base.py", line 439, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/layers/pooling.py", line 285, in call
data_format=utils.convert_data_format(data_format, 4))
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py", line 1769, in max_pool
name=name)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 1605, in _max_pool
data_format=data_format, name=name)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/home/cs231n/myVE35/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[100,64,112,112]
[[Node: gradients_1/vgg_16/pool1/MaxPool_grad/MaxPoolGrad = MaxPoolGrad[T=DT_FLOAT, data_format="NHWC", ksize=[1, 2, 2, 1], padding="VALID", strides=[1, 2, 2, 1], _device="/job:localhost/replica:0/task:0/gpu:0"](vgg_16/conv1/conv1_2/Relu, vgg_16/pool1/MaxPool, gradients_1/vgg_16/conv2/conv2_1/convolution_grad/tuple/control_dependency)]]
In [ ]:
predicted_logits
In [93]:
actual_labels
Out[93]:
[array([[0, 0, 0, ..., 1, 0, 0],
[0, 1, 0, ..., 0, 1, 0],
[0, 1, 0, ..., 0, 1, 0],
...,
[1, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 0, 0, 0],
[0, 0, 0, ..., 1, 1, 0]], dtype=int32), array([[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[1, 1, 0, ..., 0, 1, 0],
...,
[1, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 0, 0],
[0, 0, 1, ..., 0, 1, 0]], dtype=int32), array([[0, 0, 1, ..., 0, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
...,
[1, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0]], dtype=int32), array([[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
...,
[0, 0, 0, ..., 1, 1, 0],
[0, 1, 0, ..., 0, 1, 0],
[0, 0, 0, ..., 1, 1, 0]], dtype=int32), array([[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
...,
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0]], dtype=int32), array([[0, 1, 0, ..., 0, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 1, 0, ..., 0, 1, 0],
...,
[1, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0]], dtype=int32), array([[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 0, 0],
[0, 0, 0, ..., 1, 1, 0],
...,
[0, 1, 0, ..., 0, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0]], dtype=int32), array([[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 0, 0],
[0, 0, 0, ..., 1, 1, 0],
...,
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0]], dtype=int32), array([[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 1, 0, ..., 0, 1, 0],
...,
[0, 1, 0, ..., 0, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0]], dtype=int32), array([[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
[0, 0, 0, ..., 1, 1, 0],
...,
[1, 0, 0, ..., 1, 1, 0],
[0, 1, 0, ..., 0, 1, 0],
[0, 1, 0, ..., 0, 1, 0]], dtype=int32)]
In [84]:
loss_log
Out[84]:
[7.7339444,
7.4191761,
7.7160192,
7.9325938,
8.340826,
7.9754939,
7.4352045,
7.7459483,
8.023757,
7.66538]
In [ ]:
Content source: Rohisha/classify-satellite-imagery
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